{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 深度学习框架使用\n",
    "> 该notebook通过房价数据集学习深度学习框架使用 <br>\n",
    "> 根据房子属性和历史成交价预测给定属性的房子价值\n",
    "\n",
    "### pipeline\n",
    "- 前处理\n",
    "- 特征\n",
    "- 模型\n",
    "- 后处理\n",
    "- 本地验证\n",
    "\n",
    "### 相比GBDT模型（XGBoost, LightGBM, CatBoost）\n",
    "- nn需要处理NA\n",
    "- nn需要特征标准化norm\n",
    "\n",
    "\n",
    "### 回归任务常用技巧\n",
    "- 回归任务标签转换\n",
    "- 特征\n",
    "- 模型\n",
    "- 后处理\n",
    "- 模型融合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入包 Load package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import skew, pearsonr\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 配置超参数 Setup configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CFG:\n",
    "    device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "    epochs = 200\n",
    "    learning_rate = 0.002\n",
    "    batch_size = 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "\n",
    "# 准备工作 experiment prepare\n",
    "\n",
    "def seed_everything(seed):\n",
    "    \"\"\"\n",
    "    Seeds basic parameters for reproductibility of results.\n",
    "\n",
    "    Args:\n",
    "        seed (int): Number of the seed.\n",
    "    \"\"\"\n",
    "    random.seed(seed)\n",
    "    os.environ[\"PYTHONHASHSEED\"] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备数据 Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('./house-prices-advanced-regression-techniques/train.csv')\n",
    "test = pd.read_csv('./house-prices-advanced-regression-techniques/test.csv')\n",
    "submission = pd.read_csv('./house-prices-advanced-regression-techniques/sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>1456</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7917</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>1457</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>13175</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>1458</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>66.0</td>\n",
       "      <td>9042</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>2500</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>266500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>1459</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9717</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>142125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>1460</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9937</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>147500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1460 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0        1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1        2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2        3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3        4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4        5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "1455  1456          60       RL         62.0     7917   Pave   NaN      Reg   \n",
       "1456  1457          20       RL         85.0    13175   Pave   NaN      Reg   \n",
       "1457  1458          70       RL         66.0     9042   Pave   NaN      Reg   \n",
       "1458  1459          20       RL         68.0     9717   Pave   NaN      Reg   \n",
       "1459  1460          20       RL         75.0     9937   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
       "0            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "2            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "3            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "4            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "...          ...       ...  ...      ...    ...    ...         ...     ...   \n",
       "1455         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1456         Lvl    AllPub  ...        0    NaN  MnPrv         NaN       0   \n",
       "1457         Lvl    AllPub  ...        0    NaN  GdPrv        Shed    2500   \n",
       "1458         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1459         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "\n",
       "     MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0         2   2008        WD         Normal     208500  \n",
       "1         5   2007        WD         Normal     181500  \n",
       "2         9   2008        WD         Normal     223500  \n",
       "3         2   2006        WD        Abnorml     140000  \n",
       "4        12   2008        WD         Normal     250000  \n",
       "...     ...    ...       ...            ...        ...  \n",
       "1455      8   2007        WD         Normal     175000  \n",
       "1456      2   2010        WD         Normal     210000  \n",
       "1457      5   2010        WD         Normal     266500  \n",
       "1458      4   2010        WD         Normal     142125  \n",
       "1459      6   2008        WD         Normal     147500  \n",
       "\n",
       "[1460 rows x 81 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LotFrontage    259\n",
      "MasVnrArea       8\n",
      "GarageYrBlt     81\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# preprocess\n",
    "numerical_col = []\n",
    "categorical_col = []\n",
    "\n",
    "types_dict = train.columns.to_series().groupby(train.dtypes).groups\n",
    "\n",
    "for key, vol in types_dict.items():\n",
    "    if key == 'int' or key == 'float':\n",
    "        for col in range(len(vol)):\n",
    "            if vol[col] != 'SalePrice':\n",
    "                numerical_col.append(vol[col])\n",
    "    else:\n",
    "        for col in range(len(vol)):\n",
    "            categorical_col.append(vol[col])\n",
    "\n",
    "print(train[numerical_col].isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x3000 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EDA-feature\n",
    "\n",
    "plt.subplots(8, 4, figsize=(30, 30))\n",
    "\n",
    "# Plot a density plot for each variable\n",
    "for idx, col in enumerate(numerical_col[:32]):\n",
    "    ax = plt.subplot(8, 4, idx + 1)\n",
    "    ax.yaxis.set_ticklabels([])\n",
    "    sns.distplot(train[col], hist=False, axlabel=False,\n",
    "                 kde_kws={'linestyle': '-', 'color': 'blue', 'label': \"No Diabetes\"})\n",
    "    ax.set_title(col)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EDA-label\n",
    "plt.subplot(121)\n",
    "plt.hist(train['SalePrice'], bins=100);\n",
    "plt.subplot(122)\n",
    "plt.hist(np.log1p(train['SalePrice']), bins=100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# norm\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_num = train[numerical_col]\n",
    "X_num = X_num.fillna(0)\n",
    "X_num = scaler.fit_transform(X_num)\n",
    "\n",
    "one_hot = OneHotEncoder(handle_unknown='ignore')\n",
    "X_cat = one_hot.fit_transform(train[categorical_col]).toarray()\n",
    "X = np.concatenate([X_num, X_cat], axis=1)\n",
    "\n",
    "y = train['SalePrice'].values\n",
    "y = np.log1p(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset\n",
    "\n",
    "class HouseDataset(Dataset):\n",
    "    def __init__(self, X, y):\n",
    "        self.X = X\n",
    "        self.y = y\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        feature = self.X[idx]\n",
    "        label = self.y[idx]\n",
    "        return torch.tensor(feature, dtype=torch.float32), torch.tensor(label, dtype=torch.float32)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([0.2129, 0.5141, 0.2960,  ..., 0.0000, 1.0000, 0.0000]),\n",
       " tensor(12.2477))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check dataset\n",
    "dataset = HouseDataset(X, y)\n",
    "dataset[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 搭建模型 Build model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class HouseModel(nn.Module):\n",
    "    def __init__(self, input_dim):\n",
    "        super().__init__()\n",
    "        self.mlp = nn.Sequential(\n",
    "            nn.Linear(input_dim, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(512, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(256, 1),\n",
    "            # nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.mlp(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HouseModel(\n",
      "  (mlp): Sequential(\n",
      "    (0): Linear(in_features=19, out_features=512, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): Linear(in_features=512, out_features=256, bias=True)\n",
      "    (3): ReLU()\n",
      "    (4): Linear(in_features=256, out_features=1, bias=True)\n",
      "  )\n",
      ")\n",
      "torch.Size([2, 1])\n"
     ]
    }
   ],
   "source": [
    "# 测试模型 test model\n",
    "model = HouseModel(input_dim=19)\n",
    "print(model)\n",
    "\n",
    "x_random = torch.randn(2, 19)  # (batch_size, 特征数量)\n",
    "model = HouseModel(19)\n",
    "y_random = model(x_random)\n",
    "print(y_random.shape)\n",
    "assert y_random.shape == (2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: graphviz in d:\\miniconda3\\envs\\pytorch2\\lib\\site-packages (0.20.1)\n",
      "Requirement already satisfied: torchviz in d:\\miniconda3\\envs\\pytorch2\\lib\\site-packages (0.0.2)\n",
      "Requirement already satisfied: torch in d:\\miniconda3\\envs\\pytorch2\\lib\\site-packages (from torchviz) (2.1.1+cu118)\n",
      "Requirement already satisfied: filelock in d:\\miniconda3\\envs\\pytorch2\\lib\\site-packages (from torch->torchviz) (3.9.0)\n",
      "Requirement already satisfied: typing-extensions in d:\\miniconda3\\envs\\pytorch2\\lib\\site-packages (from torch->torchviz) (4.4.0)\n",
      "Requirement already satisfied: sympy in d:\\miniconda3\\envs\\pytorch2\\lib\\site-packages (from torch->torchviz) (1.12)\n",
      "Requirement already satisfied: networkx in d:\\miniconda3\\envs\\pytorch2\\lib\\site-packages (from torch->torchviz) (3.0)\n",
      "Requirement already satisfied: jinja2 in d:\\miniconda3\\envs\\pytorch2\\lib\\site-packages (from torch->torchviz) (3.1.2)\n",
      "Requirement already satisfied: fsspec in d:\\miniconda3\\envs\\pytorch2\\lib\\site-packages (from torch->torchviz) (2023.10.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in d:\\miniconda3\\envs\\pytorch2\\lib\\site-packages (from jinja2->torch->torchviz) (2.1.3)\n",
      "Requirement already satisfied: mpmath>=0.19 in d:\\miniconda3\\envs\\pytorch2\\lib\\site-packages (from sympy->torch->torchviz) (1.3.0)\n",
      "HouseModel(\n",
      "  (mlp): Sequential(\n",
      "    (0): Linear(in_features=19, out_features=512, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): Linear(in_features=512, out_features=256, bias=True)\n",
      "    (3): ReLU()\n",
      "    (4): Linear(in_features=256, out_features=1, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'data\\\\Digraph.gv.png'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!pip install graphviz torchviz\n",
    "MyConvNet = HouseModel(input_dim=19)\n",
    "print(MyConvNet)\n",
    "\n",
    "from torchviz import make_dot\n",
    "x = torch.randn(2, 19).requires_grad_(True)  # 定义一个网络的输入值\n",
    "y = MyConvNet(x)    # 获取网络的预测值\n",
    " \n",
    "MyConvNetVis = make_dot(y, params=dict(list(MyConvNet.named_parameters()) + [('x', x)]))\n",
    "MyConvNetVis.format = \"png\"\n",
    "\n",
    "MyConvNetVis.directory = \"data\"\n",
    "# 生成文件\n",
    "MyConvNetVis.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练和验证 Train and Validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1168, 9236) (1168,) (292, 9236) (292,)\n",
      "torch.Size([9236]) torch.Size([])\n",
      "torch.Size([256, 9236]) torch.Size([256])\n"
     ]
    }
   ],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "train_dataset = HouseDataset(X_train, y_train)\n",
    "valid_dataset = HouseDataset(X_valid, y_valid)\n",
    "train_loader = DataLoader(train_dataset, batch_size=CFG.batch_size, shuffle=True)\n",
    "valid_loader = DataLoader(valid_dataset, batch_size=CFG.batch_size, shuffle=False)\n",
    "\n",
    "print(X_train.shape, y_train.shape, X_valid.shape, y_valid.shape)\n",
    "print(train_dataset[0][0].shape, train_dataset[0][1].shape)\n",
    "\n",
    "for feature, label in valid_loader:\n",
    "    print(feature.size(), label.size())\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T15:36:05.656919Z",
     "iopub.status.busy": "2023-11-03T15:36:05.656609Z",
     "iopub.status.idle": "2023-11-03T15:36:23.027467Z",
     "shell.execute_reply": "2023-11-03T15:36:23.026422Z",
     "shell.execute_reply.started": "2023-11-03T15:36:05.656895Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1, Train Loss: 51.2549934387207, Valid Score: 4.103856374982225\n",
      "Epoch: 2, Train Loss: 30.547569274902344, Valid Score: 3.0912760403425903\n",
      "Epoch: 3, Train Loss: 10.260889053344727, Valid Score: 4.01270578384122\n",
      "Epoch: 4, Train Loss: 6.366034030914307, Valid Score: 1.7284283300314043\n",
      "Epoch: 5, Train Loss: 6.022409915924072, Valid Score: 2.0208482652692767\n",
      "Epoch: 6, Train Loss: 0.5838884115219116, Valid Score: 1.3105532335945387\n",
      "Epoch: 7, Train Loss: 2.667872428894043, Valid Score: 1.6577388421188468\n",
      "Epoch: 8, Train Loss: 1.2422651052474976, Valid Score: 0.8709579000021744\n",
      "Epoch: 9, Train Loss: 0.6258655190467834, Valid Score: 0.7587860439366358\n",
      "Epoch: 10, Train Loss: 0.9877666234970093, Valid Score: 1.1725782868024943\n",
      "Epoch: 11, Train Loss: 0.3975347876548767, Valid Score: 0.5501576651653826\n",
      "Epoch: 12, Train Loss: 0.3188168406486511, Valid Score: 0.6763009485756585\n",
      "Epoch: 13, Train Loss: 0.4744464159011841, Valid Score: 0.8407079543881595\n",
      "Epoch: 14, Train Loss: 0.24777095019817352, Valid Score: 0.5195232982875924\n",
      "Epoch: 15, Train Loss: 0.1859840303659439, Valid Score: 0.7274925228766426\n",
      "Epoch: 16, Train Loss: 0.2850418984889984, Valid Score: 0.6671351695304387\n",
      "Epoch: 17, Train Loss: 0.1926424652338028, Valid Score: 0.5702500028716221\n",
      "Epoch: 18, Train Loss: 0.1935253143310547, Valid Score: 0.708461041569963\n",
      "Epoch: 19, Train Loss: 0.17220330238342285, Valid Score: 0.5975755870069279\n",
      "Epoch: 20, Train Loss: 0.2002202272415161, Valid Score: 0.6367501962445503\n",
      "Epoch: 21, Train Loss: 0.17838606238365173, Valid Score: 0.6500010310581221\n",
      "Epoch: 22, Train Loss: 0.15038737654685974, Valid Score: 0.6055211336134482\n",
      "Epoch: 23, Train Loss: 0.15006713569164276, Valid Score: 0.6611919949865792\n",
      "Epoch: 24, Train Loss: 0.18873968720436096, Valid Score: 0.6219323620138988\n",
      "Epoch: 25, Train Loss: 0.1610608994960785, Valid Score: 0.6302728104338609\n",
      "Epoch: 26, Train Loss: 0.15988601744174957, Valid Score: 0.6361287955945666\n",
      "Epoch: 27, Train Loss: 0.18560320138931274, Valid Score: 0.6233525555022967\n",
      "Epoch: 28, Train Loss: 0.16361963748931885, Valid Score: 0.6295732006948067\n",
      "Epoch: 29, Train Loss: 0.1537647247314453, Valid Score: 0.635883695460526\n",
      "Epoch: 30, Train Loss: 0.20154161751270294, Valid Score: 0.6379858724139688\n",
      "Epoch: 31, Train Loss: 0.1532021462917328, Valid Score: 0.631128896533527\n",
      "Epoch: 32, Train Loss: 0.1587667614221573, Valid Score: 0.63697658500128\n",
      "Epoch: 33, Train Loss: 0.13070353865623474, Valid Score: 0.6301910391338262\n",
      "Epoch: 34, Train Loss: 0.17303544282913208, Valid Score: 0.6283360665191635\n",
      "Epoch: 35, Train Loss: 0.1625073105096817, Valid Score: 0.634720449336539\n",
      "Epoch: 36, Train Loss: 0.14618004858493805, Valid Score: 0.6199665458663431\n",
      "Epoch: 37, Train Loss: 0.1597737967967987, Valid Score: 0.626452464362366\n",
      "Epoch: 38, Train Loss: 0.13374613225460052, Valid Score: 0.634665678972119\n",
      "Epoch: 39, Train Loss: 0.1340455263853073, Valid Score: 0.6342854825374362\n",
      "Epoch: 40, Train Loss: 0.1318153291940689, Valid Score: 0.6294371484625635\n",
      "Epoch: 41, Train Loss: 0.1659862995147705, Valid Score: 0.6227989126149617\n",
      "Epoch: 42, Train Loss: 0.15477128326892853, Valid Score: 0.634253865050006\n",
      "Epoch: 43, Train Loss: 0.12376381456851959, Valid Score: 0.6372393634251761\n",
      "Epoch: 44, Train Loss: 0.17812910676002502, Valid Score: 0.6356858158484454\n",
      "Epoch: 45, Train Loss: 0.17159734666347504, Valid Score: 0.6204415198303808\n",
      "Epoch: 46, Train Loss: 0.16422060132026672, Valid Score: 0.6399701377811208\n",
      "Epoch: 47, Train Loss: 0.12609058618545532, Valid Score: 0.6294757311676016\n",
      "Epoch: 48, Train Loss: 0.14461027085781097, Valid Score: 0.6352763398179488\n",
      "Epoch: 49, Train Loss: 0.19280824065208435, Valid Score: 0.6244261174878848\n",
      "Epoch: 50, Train Loss: 0.15203727781772614, Valid Score: 0.6355918842352393\n",
      "Epoch: 51, Train Loss: 0.15907134115695953, Valid Score: 0.6364171189210107\n",
      "Epoch: 52, Train Loss: 0.14851543307304382, Valid Score: 0.6372936847826715\n",
      "Epoch: 53, Train Loss: 0.13311359286308289, Valid Score: 0.627784475038464\n",
      "Epoch: 54, Train Loss: 0.18612642586231232, Valid Score: 0.6209268349847975\n",
      "Epoch: 55, Train Loss: 0.18017011880874634, Valid Score: 0.6376529394361867\n",
      "Epoch: 56, Train Loss: 0.1628222018480301, Valid Score: 0.6287318884624976\n",
      "Epoch: 57, Train Loss: 0.20798933506011963, Valid Score: 0.6247526230039274\n",
      "Epoch: 58, Train Loss: 0.16436535120010376, Valid Score: 0.637712818537567\n",
      "Epoch: 59, Train Loss: 0.19724977016448975, Valid Score: 0.6355477194003453\n",
      "Epoch: 60, Train Loss: 0.16299620270729065, Valid Score: 0.6240504336977967\n",
      "Epoch: 61, Train Loss: 0.1411740779876709, Valid Score: 0.6390018019630115\n",
      "Epoch: 62, Train Loss: 0.15997666120529175, Valid Score: 0.6294171495692916\n",
      "Epoch: 63, Train Loss: 0.13730596005916595, Valid Score: 0.6522407909402818\n",
      "Epoch: 64, Train Loss: 0.1356457620859146, Valid Score: 0.6110615014311901\n",
      "Epoch: 65, Train Loss: 0.16115078330039978, Valid Score: 0.6469137867422032\n",
      "Epoch: 66, Train Loss: 0.133976012468338, Valid Score: 0.6153705081554061\n",
      "Epoch: 67, Train Loss: 0.14321918785572052, Valid Score: 0.6579749315509722\n",
      "Epoch: 68, Train Loss: 0.1694052517414093, Valid Score: 0.5971059663603842\n",
      "Epoch: 69, Train Loss: 0.15489162504673004, Valid Score: 0.6671833105141058\n",
      "Epoch: 70, Train Loss: 0.15537197887897491, Valid Score: 0.5990044679530222\n",
      "Epoch: 71, Train Loss: 0.1832032948732376, Valid Score: 0.6638076774025118\n",
      "Epoch: 72, Train Loss: 0.18905289471149445, Valid Score: 0.6269542694407497\n",
      "Epoch: 73, Train Loss: 0.18786433339118958, Valid Score: 0.6166587848270286\n",
      "Epoch: 74, Train Loss: 0.20476004481315613, Valid Score: 0.6475063943122815\n",
      "Epoch: 75, Train Loss: 0.15040089190006256, Valid Score: 0.6203986718007186\n",
      "Epoch: 76, Train Loss: 0.16334103047847748, Valid Score: 0.6526466154056205\n",
      "Epoch: 77, Train Loss: 0.1834242194890976, Valid Score: 0.6169873273818344\n",
      "Epoch: 78, Train Loss: 0.16905374825000763, Valid Score: 0.6726755259862881\n",
      "Epoch: 79, Train Loss: 0.14082086086273193, Valid Score: 0.5750922694395744\n",
      "Epoch: 80, Train Loss: 0.15194730460643768, Valid Score: 0.6916049378216114\n",
      "Epoch: 81, Train Loss: 0.19753515720367432, Valid Score: 0.6026329213811025\n",
      "Epoch: 82, Train Loss: 0.18252943456172943, Valid Score: 0.6144426018610872\n",
      "Epoch: 83, Train Loss: 0.16926629841327667, Valid Score: 0.7062762709572454\n",
      "Epoch: 84, Train Loss: 0.152436763048172, Valid Score: 0.5610980126495432\n",
      "Epoch: 85, Train Loss: 0.24817520380020142, Valid Score: 0.6757079127995905\n",
      "Epoch: 86, Train Loss: 0.168088898062706, Valid Score: 0.6117578054488457\n",
      "Epoch: 87, Train Loss: 0.12404610216617584, Valid Score: 0.6332625190361229\n",
      "Epoch: 88, Train Loss: 0.1914215087890625, Valid Score: 0.6463702576889163\n",
      "Epoch: 89, Train Loss: 0.19484181702136993, Valid Score: 0.6071880593252129\n",
      "Epoch: 90, Train Loss: 0.1428888589143753, Valid Score: 0.6810495453190561\n",
      "Epoch: 91, Train Loss: 0.18782375752925873, Valid Score: 0.6071328601451951\n",
      "Epoch: 92, Train Loss: 0.20415443181991577, Valid Score: 0.6051848328855756\n",
      "Epoch: 93, Train Loss: 0.1655932068824768, Valid Score: 0.712433727210064\n",
      "Epoch: 94, Train Loss: 0.14526057243347168, Valid Score: 0.5789720149839167\n",
      "Epoch: 95, Train Loss: 0.17818106710910797, Valid Score: 0.6677978334328571\n",
      "Epoch: 96, Train Loss: 0.17083749175071716, Valid Score: 0.6010867183568024\n",
      "Epoch: 97, Train Loss: 0.14401650428771973, Valid Score: 0.6492547213253927\n",
      "Epoch: 98, Train Loss: 0.20232321321964264, Valid Score: 0.6611708871769241\n",
      "Epoch: 99, Train Loss: 0.14893989264965057, Valid Score: 0.5863118564792781\n",
      "Epoch: 100, Train Loss: 0.19488924741744995, Valid Score: 0.6534617448231749\n",
      "Epoch: 101, Train Loss: 0.18504510819911957, Valid Score: 0.6361617245877007\n",
      "Epoch: 102, Train Loss: 0.19255758821964264, Valid Score: 0.607027335102098\n",
      "Epoch: 103, Train Loss: 0.19727782905101776, Valid Score: 0.6646107758115661\n",
      "Epoch: 104, Train Loss: 0.18320076167583466, Valid Score: 0.6014559475800472\n",
      "Epoch: 105, Train Loss: 0.17621049284934998, Valid Score: 0.6354494745669117\n",
      "Epoch: 106, Train Loss: 0.1815185397863388, Valid Score: 0.6436321359529397\n",
      "Epoch: 107, Train Loss: 0.16863830387592316, Valid Score: 0.5931142080950806\n",
      "Epoch: 108, Train Loss: 0.15677250921726227, Valid Score: 0.6949118369133359\n",
      "Epoch: 109, Train Loss: 0.15757067501544952, Valid Score: 0.5902302128864624\n",
      "Epoch: 110, Train Loss: 0.1961834579706192, Valid Score: 0.6184946564907005\n",
      "Epoch: 111, Train Loss: 0.17035795748233795, Valid Score: 0.6474744689476856\n",
      "Epoch: 112, Train Loss: 0.17576229572296143, Valid Score: 0.6553729391078889\n",
      "Epoch: 113, Train Loss: 0.12944748997688293, Valid Score: 0.6253790002574335\n",
      "Epoch: 114, Train Loss: 0.15723495185375214, Valid Score: 0.6139538437938347\n",
      "Epoch: 115, Train Loss: 0.15329138934612274, Valid Score: 0.6786767640925632\n",
      "Epoch: 116, Train Loss: 0.14292855560779572, Valid Score: 0.616315980975843\n",
      "Epoch: 117, Train Loss: 0.15569180250167847, Valid Score: 0.6255852525252676\n",
      "Epoch: 118, Train Loss: 0.16826094686985016, Valid Score: 0.6813930250858568\n",
      "Epoch: 119, Train Loss: 0.18133585155010223, Valid Score: 0.5902700987048417\n",
      "Epoch: 120, Train Loss: 0.13597789406776428, Valid Score: 0.662168905275941\n",
      "Epoch: 121, Train Loss: 0.14353269338607788, Valid Score: 0.6236947928967224\n",
      "Epoch: 122, Train Loss: 0.19541649520397186, Valid Score: 0.5959721898877007\n",
      "Epoch: 123, Train Loss: 0.1911412477493286, Valid Score: 0.6154579896137194\n",
      "Epoch: 124, Train Loss: 0.18445159494876862, Valid Score: 0.6734299078336381\n",
      "Epoch: 125, Train Loss: 0.1696500927209854, Valid Score: 0.5994926424749686\n",
      "Epoch: 126, Train Loss: 0.17472776770591736, Valid Score: 0.6363409238205698\n",
      "Epoch: 127, Train Loss: 0.1422813981771469, Valid Score: 0.6074497597469909\n",
      "Epoch: 128, Train Loss: 0.14019203186035156, Valid Score: 0.6100986417649288\n",
      "Epoch: 129, Train Loss: 0.16547554731369019, Valid Score: 0.6681781470225825\n",
      "Epoch: 130, Train Loss: 0.17189505696296692, Valid Score: 0.6331647364211977\n",
      "Epoch: 131, Train Loss: 0.14604923129081726, Valid Score: 0.6115942703610865\n",
      "Epoch: 132, Train Loss: 0.15931089222431183, Valid Score: 0.6466974665833519\n",
      "Epoch: 133, Train Loss: 0.127701073884964, Valid Score: 0.6419637469232341\n",
      "Epoch: 134, Train Loss: 0.17073111236095428, Valid Score: 0.5618822478219754\n",
      "Epoch: 135, Train Loss: 0.15951794385910034, Valid Score: 0.7073262105975081\n",
      "Epoch: 136, Train Loss: 0.16060425341129303, Valid Score: 0.6333783232526929\n",
      "Epoch: 137, Train Loss: 0.17912374436855316, Valid Score: 0.6110495682728221\n",
      "Epoch: 138, Train Loss: 0.15599662065505981, Valid Score: 0.6261167447366047\n",
      "Epoch: 139, Train Loss: 0.15451405942440033, Valid Score: 0.6675461142620636\n",
      "Epoch: 140, Train Loss: 0.14395016431808472, Valid Score: 0.5952779773845932\n",
      "Epoch: 141, Train Loss: 0.18433615565299988, Valid Score: 0.6439224419171906\n",
      "Epoch: 142, Train Loss: 0.14129042625427246, Valid Score: 0.6729953060986538\n",
      "Epoch: 143, Train Loss: 0.18541036546230316, Valid Score: 0.6034263529032835\n",
      "Epoch: 144, Train Loss: 0.17055991291999817, Valid Score: 0.6276209327824624\n",
      "Epoch: 145, Train Loss: 0.18599839508533478, Valid Score: 0.6146127900864481\n",
      "Epoch: 146, Train Loss: 0.15545077621936798, Valid Score: 0.6362356761164294\n",
      "Epoch: 147, Train Loss: 0.15415403246879578, Valid Score: 0.5626492873111199\n",
      "Epoch: 148, Train Loss: 0.14084698259830475, Valid Score: 0.6908191743492474\n",
      "Epoch: 149, Train Loss: 0.17387332022190094, Valid Score: 0.7033599096658952\n",
      "Epoch: 150, Train Loss: 0.18199923634529114, Valid Score: 0.5865217641113768\n",
      "Epoch: 151, Train Loss: 0.17604506015777588, Valid Score: 0.6351851378484826\n",
      "Epoch: 152, Train Loss: 0.17414526641368866, Valid Score: 0.6634454701589348\n",
      "Epoch: 153, Train Loss: 0.14910639822483063, Valid Score: 0.5970242162685453\n",
      "Epoch: 154, Train Loss: 0.18122637271881104, Valid Score: 0.6443348284860161\n",
      "Epoch: 155, Train Loss: 0.17343199253082275, Valid Score: 0.6569059636748438\n",
      "Epoch: 156, Train Loss: 0.16306835412979126, Valid Score: 0.6417747576012363\n",
      "Epoch: 157, Train Loss: 0.2070428431034088, Valid Score: 0.6414675327311218\n",
      "Epoch: 158, Train Loss: 0.14634642004966736, Valid Score: 0.6314579084824373\n",
      "Epoch: 159, Train Loss: 0.1419079303741455, Valid Score: 0.6412061170217082\n",
      "Epoch: 160, Train Loss: 0.1759951263666153, Valid Score: 0.5766994861225512\n",
      "Epoch: 161, Train Loss: 0.1845521181821823, Valid Score: 0.5875513273783499\n",
      "Epoch: 162, Train Loss: 0.17405448853969574, Valid Score: 0.6219511407753101\n",
      "Epoch: 163, Train Loss: 0.18609946966171265, Valid Score: 0.6534952896778016\n",
      "Epoch: 164, Train Loss: 0.1430954486131668, Valid Score: 0.684219887432935\n",
      "Epoch: 165, Train Loss: 0.17690236866474152, Valid Score: 0.6386265429778549\n",
      "Epoch: 166, Train Loss: 0.16661497950553894, Valid Score: 0.5943738916732514\n",
      "Epoch: 167, Train Loss: 0.18362396955490112, Valid Score: 0.617828864365414\n",
      "Epoch: 168, Train Loss: 0.162362203001976, Valid Score: 0.6749480026946661\n",
      "Epoch: 169, Train Loss: 0.13325680792331696, Valid Score: 0.6261861816272751\n",
      "Epoch: 170, Train Loss: 0.13043011724948883, Valid Score: 0.6028466412756153\n",
      "Epoch: 171, Train Loss: 0.18992047011852264, Valid Score: 0.6626939471937803\n",
      "Epoch: 172, Train Loss: 0.15196095407009125, Valid Score: 0.6182507306037612\n",
      "Epoch: 173, Train Loss: 0.20172621309757233, Valid Score: 0.6084324358870462\n",
      "Epoch: 174, Train Loss: 0.1617160439491272, Valid Score: 0.6158674133202477\n",
      "Epoch: 175, Train Loss: 0.14391092956066132, Valid Score: 0.587535173985685\n",
      "Epoch: 176, Train Loss: 0.16445760428905487, Valid Score: 0.66383279382297\n",
      "Epoch: 177, Train Loss: 0.17643578350543976, Valid Score: 0.650272935317288\n",
      "Epoch: 178, Train Loss: 0.18174085021018982, Valid Score: 0.6607441079752592\n",
      "Epoch: 179, Train Loss: 0.16363899409770966, Valid Score: 0.5873234940346861\n",
      "Epoch: 180, Train Loss: 0.12685388326644897, Valid Score: 0.5779821513717148\n",
      "Epoch: 181, Train Loss: 0.15999692678451538, Valid Score: 0.6675034796561077\n",
      "Epoch: 182, Train Loss: 0.14086511731147766, Valid Score: 0.7119140515462337\n",
      "Epoch: 183, Train Loss: 0.17129269242286682, Valid Score: 0.6287134501514693\n",
      "Epoch: 184, Train Loss: 0.18155910074710846, Valid Score: 0.591582820081689\n",
      "Epoch: 185, Train Loss: 0.14358772337436676, Valid Score: 0.631059510646643\n",
      "Epoch: 186, Train Loss: 0.1730065941810608, Valid Score: 0.6691889246960487\n",
      "Epoch: 187, Train Loss: 0.15867716073989868, Valid Score: 0.6481863901061639\n",
      "Epoch: 188, Train Loss: 0.14436960220336914, Valid Score: 0.5558365563361701\n",
      "Epoch: 189, Train Loss: 0.1414928436279297, Valid Score: 0.5977976803536442\n",
      "Epoch: 190, Train Loss: 0.204922616481781, Valid Score: 0.6782855599637797\n",
      "Epoch: 191, Train Loss: 0.19322459399700165, Valid Score: 0.6598244920143034\n",
      "Epoch: 192, Train Loss: 0.18618418276309967, Valid Score: 0.5935572559320509\n",
      "Epoch: 193, Train Loss: 0.136642724275589, Valid Score: 0.6087433472556707\n",
      "Epoch: 194, Train Loss: 0.12597590684890747, Valid Score: 0.6592934314710507\n",
      "Epoch: 195, Train Loss: 0.18038809299468994, Valid Score: 0.6818081256663197\n",
      "Epoch: 196, Train Loss: 0.20783831179141998, Valid Score: 0.6454290147471237\n",
      "Epoch: 197, Train Loss: 0.15768328309059143, Valid Score: 0.583721370626958\n",
      "Epoch: 198, Train Loss: 0.15049926936626434, Valid Score: 0.5996308041687991\n",
      "Epoch: 199, Train Loss: 0.1432567685842514, Valid Score: 0.6414432500184242\n",
      "Epoch: 200, Train Loss: 0.18017955124378204, Valid Score: 0.6896439655198396\n"
     ]
    }
   ],
   "source": [
    "model = HouseModel(X.shape[1])\n",
    "model.to(CFG.device)\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=CFG.learning_rate)\n",
    "\n",
    "for n in range(CFG.epochs):\n",
    "    # train\n",
    "    model.train()\n",
    "    for feature, label in train_loader:\n",
    "        y_pred = model(feature.to(CFG.device))\n",
    "        loss = criterion(y_pred, label.to(CFG.device))\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    # valid\n",
    "    y_preds = []\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        for valid_feature, valid_label in valid_loader:\n",
    "            y_pred = model(valid_feature.to(CFG.device)).squeeze(-1)\n",
    "            y_preds.append(y_pred.detach().cpu().numpy())\n",
    "\n",
    "    y_preds = np.concatenate(y_preds, 0)\n",
    "    mse = np.sqrt(mean_squared_error(y_valid, y_preds))\n",
    "    print(f\"Epoch: {n + 1}, Train Loss: {loss.item()}, Valid Score: {mse}\")\n",
    "\n",
    "# save the model\n",
    "torch.save(model, 'my_model.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T15:36:23.02931Z",
     "iopub.status.busy": "2023-11-03T15:36:23.028897Z",
     "iopub.status.idle": "2023-11-03T15:36:23.035415Z",
     "shell.execute_reply": "2023-11-03T15:36:23.034433Z",
     "shell.execute_reply.started": "2023-11-03T15:36:23.029272Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 9236)\n",
      "(1460,)\n"
     ]
    }
   ],
   "source": [
    "# training process visual\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 检查结果 Check the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T15:36:23.037033Z",
     "iopub.status.busy": "2023-11-03T15:36:23.036714Z",
     "iopub.status.idle": "2023-11-03T15:36:23.37329Z",
     "shell.execute_reply": "2023-11-03T15:36:23.372354Z",
     "shell.execute_reply.started": "2023-11-03T15:36:23.036993Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x174534bdf60>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_valid.reshape(-1), label='truth')\n",
    "plt.plot(y_preds.reshape(-1), label='prediction')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T15:36:23.374806Z",
     "iopub.status.busy": "2023-11-03T15:36:23.37446Z",
     "iopub.status.idle": "2023-11-03T15:36:23.428019Z",
     "shell.execute_reply": "2023-11-03T15:36:23.42707Z",
     "shell.execute_reply.started": "2023-11-03T15:36:23.374779Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>2915</td>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1936</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>2916</td>\n",
       "      <td>160</td>\n",
       "      <td>RM</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1894</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2917</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2918</td>\n",
       "      <td>85</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>10441</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2919</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>9627</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1459 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0     1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1     1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2     1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "3     1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
       "4     1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "1454  2915         160       RM         21.0     1936   Pave   NaN      Reg   \n",
       "1455  2916         160       RM         21.0     1894   Pave   NaN      Reg   \n",
       "1456  2917          20       RL        160.0    20000   Pave   NaN      Reg   \n",
       "1457  2918          85       RL         62.0    10441   Pave   NaN      Reg   \n",
       "1458  2919          60       RL         74.0     9627   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence  \\\n",
       "0            Lvl    AllPub  ...         120        0    NaN  MnPrv   \n",
       "1            Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "2            Lvl    AllPub  ...           0        0    NaN  MnPrv   \n",
       "3            Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "4            HLS    AllPub  ...         144        0    NaN    NaN   \n",
       "...          ...       ...  ...         ...      ...    ...    ...   \n",
       "1454         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "1455         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "1456         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "1457         Lvl    AllPub  ...           0        0    NaN  MnPrv   \n",
       "1458         Lvl    AllPub  ...           0        0    NaN    NaN   \n",
       "\n",
       "     MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0            NaN       0      6    2010        WD         Normal  \n",
       "1           Gar2   12500      6    2010        WD         Normal  \n",
       "2            NaN       0      3    2010        WD         Normal  \n",
       "3            NaN       0      6    2010        WD         Normal  \n",
       "4            NaN       0      1    2010        WD         Normal  \n",
       "...          ...     ...    ...     ...       ...            ...  \n",
       "1454         NaN       0      6    2006        WD         Normal  \n",
       "1455         NaN       0      4    2006        WD        Abnorml  \n",
       "1456         NaN       0      9    2006        WD        Abnorml  \n",
       "1457        Shed     700      7    2006        WD         Normal  \n",
       "1458         NaN       0     11    2006        WD         Normal  \n",
       "\n",
       "[1459 rows x 80 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv(r'./house-prices-advanced-regression-techniques/test.csv')\n",
    "submission_id = df_test[\"Id\"].copy()\n",
    "\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-03T15:36:23.430163Z",
     "iopub.status.busy": "2023-11-03T15:36:23.429488Z",
     "iopub.status.idle": "2023-11-03T15:36:23.517113Z",
     "shell.execute_reply": "2023-11-03T15:36:23.516083Z",
     "shell.execute_reply.started": "2023-11-03T15:36:23.430125Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>73844.640625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>66215.882812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>94920.632812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>107683.148438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>76539.085938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>2915</td>\n",
       "      <td>131087.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>2916</td>\n",
       "      <td>121787.203125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2917</td>\n",
       "      <td>114143.335938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2918</td>\n",
       "      <td>76667.148438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2919</td>\n",
       "      <td>81141.460938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1459 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id      SalePrice\n",
       "0     1461   73844.640625\n",
       "1     1462   66215.882812\n",
       "2     1463   94920.632812\n",
       "3     1464  107683.148438\n",
       "4     1465   76539.085938\n",
       "...    ...            ...\n",
       "1454  2915  131087.187500\n",
       "1455  2916  121787.203125\n",
       "1456  2917  114143.335938\n",
       "1457  2918   76667.148438\n",
       "1458  2919   81141.460938\n",
       "\n",
       "[1459 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['SalePrice'] = 0\n",
    "df_test[numerical_col] = df_test[numerical_col].fillna(0)\n",
    "x_test_num = scaler.fit_transform(df_test[numerical_col])\n",
    "x_test_cat = one_hot.transform(df_test[categorical_col]).toarray()\n",
    "x_test = np.concatenate([x_test_num, x_test_cat], axis=1)\n",
    "\n",
    "\n",
    "x_test = torch.tensor(x_test).float().to(CFG.device)\n",
    "\n",
    "# model = HouseModel(input_dim=19)\n",
    "model=torch.load('my_model.pth') # 导入网络的参数\n",
    "\n",
    "\n",
    "\n",
    "y_submission = model(x_test).squeeze()  # Make it into array\n",
    "y_submission = torch.exp(y_submission)  # Scale back to normal value\n",
    "y_submission = y_submission.cpu().detach().numpy()  # Change to numpy\n",
    "submission = pd.DataFrame({\"Id\": submission_id, \"SalePrice\": y_submission})  # Make dataframe for submissino\n",
    "submission.to_csv('submission.csv', index=False)  # Put into csv file\n",
    "\n",
    "submission"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 改进建议\n",
    "- 如何增加更多特征？\n",
    "- 如何进行模型融合？\n",
    "- 可以尝试使用更深MLP网络\n",
    "- 可以使用传统机器学习方法\n",
    "- 可以使用TTA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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